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Cooperative game theory and last addition method in the allocation of firm energy rights

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  • Faria, Victor. A.D.
  • de Queiroz, Anderson Rodrigo
  • Lima, Luana M.M.
  • Lima, José W.M.

Abstract

The firm energy rights of a hydro plant is a parameter used in some electricity markets to define the maximum amount of energy that a power plant can trade through contracts. In a centralized dispatch scheme, the coordinated operation of the hydro plants generates a synergetic gain in the system firm energy, in this setting, a question that often arises is how to fairly allocate this energy among each hydro plant. This work proposes a formulation to compute the firm energy rights of hydro plants using cooperative game theory and the last addition allocation method. The main goal is to integrate the interests of hydro agents with the needs of the regulatory agencies, searching in the core of the game for solutions that give the right incentives to the optimal system development. In order to make simulations of real instances possible, it is proposed a reformulation of the traditional mixed integer linear programming model that computes the core constraints, which induces a significant speed-up of the algorithm solution time. It is shown an application of the proposed methodology to a real instance representing the Brazilian electric power system.

Suggested Citation

  • Faria, Victor. A.D. & de Queiroz, Anderson Rodrigo & Lima, Luana M.M. & Lima, José W.M., 2018. "Cooperative game theory and last addition method in the allocation of firm energy rights," Applied Energy, Elsevier, vol. 226(C), pages 905-915.
    • Handle: RePEc:eee:appene:v:226:y:2018:i:c:p:905-915
    DOI: 10.1016/j.apenergy.2018.06.065
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    References listed on IDEAS

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    3. Geng, Xinmin & Zhou, Ye & Zhao, Weiqiang & Shi, Li & Chen, Diyi & Bi, Xiaojian & Xu, Beibei, 2024. "Pricing ancillary service of a Francis hydroelectric generating system to promote renewable energy integration in a clean energy base: Tariff compensation of deep peak regulation," Renewable Energy, Elsevier, vol. 226(C).
    4. Mei, Jie & Chen, Chen & Wang, Jianhui & Kirtley, James L., 2019. "Coalitional game theory based local power exchange algorithm for networked microgrids," Applied Energy, Elsevier, vol. 239(C), pages 133-141.
    5. Ali Zarei & Sayed-Farhad Mousavi & Madjid Eshaghi Gordji & Hojat Karami, 2019. "Optimal Reservoir Operation Using Bat and Particle Swarm Algorithm and Game Theory Based on Optimal Water Allocation among Consumers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3071-3093, July.
    6. de Queiroz, Anderson Rodrigo & Faria, Victor A.D. & Lima, Luana M.M. & Lima, José W.M., 2019. "Hydropower revenues under the threat of climate change in Brazil," Renewable Energy, Elsevier, vol. 133(C), pages 873-882.
    7. Zhao, Leilei & Xue, Yixun & Sun, Hongbin & Du, Yuan & Chang, Xinyue & Su, Jia & Li, Zening, 2023. "Benefit allocation for combined heat and power dispatch considering mutual trust," Applied Energy, Elsevier, vol. 345(C).

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